Marketing attribution capturing synergistic effects between channels

ABSTRACT

Systems and methods are described for a causal marketing attribution process that includes the receiving of a plurality of marketing events associated with a customer and computing a sum of a plurality of channel-specific terms corresponding to the plurality of marketing events, wherein each of the plurality of channel-specific terms comprises a channel-specific base parameter and a channel-specific decay parameter. Additionally, the causal marketing attribution process computes a sum of a plurality of interaction terms, wherein each interaction term comprises a product of a pair of channel-specific terms, and determines a probability of a target outcome for the customer based on the sum of the plurality of channel-specific terms and the sum of the plurality of interaction terms.

BACKGROUND

The following relates generally to data analytics and more specificallyto causal marketing attribution.

Marketing refers to activities taken by companies and individuals toencourage potential customers to purchase products or services.Marketing may take a variety of different forms, which may be referredto as marketing channels. A person or company may employ a variety ofdifferent marketing channels such as email, television, display, andsocial media to encourage sales.

In many cases, the influence of each channel is difficult to detect.Thus, marketing efforts may be misdirected to channels that areinefficient or that have little impact on potential purchasers.Inefficient matching between products and customers may result in lossesof time and sales. Thus, there is a need for improved systems andmethods to determine and interpret the influence of various marketingchannels on customers' purchase decisions.

SUMMARY

A method, apparatus, and non-transitory computer readable medium forcausal marketing attribution are described. Embodiments of the method,apparatus, and non-transitory computer readable medium may identify aplurality of marketing events associated with a customer, compute a sumof a plurality of channel-specific terms corresponding to the pluralityof marketing events, wherein each of the plurality of channel-specificterms comprises a channel-specific base parameter and a channel-specificdecay parameter, compute a sum of a plurality of interaction terms,wherein each interaction term comprises a product of a pair ofchannel-specific terms, determine a probability of a target outcome forthe customer based on the sum of the plurality of channel-specific termsand the sum of the plurality of interaction terms, and presenting amarketing event to the customer based at least in part on theprobability of the target outcome.

A method, apparatus, and non-transitory computer readable medium forcausal marketing attribution are described. Embodiments of the method,apparatus, and non-transitory computer readable medium may receive aplurality of marketing events and corresponding outcomes associated witha plurality of customers, identify a marketing attribution modelcomprising a customer-independent baseline parameter, a plurality ofcustomer-heterogeneity parameters, a sum of a plurality ofchannel-specific terms, and a sum of a plurality of interaction terms,wherein each of the channel-specific terms is based on achannel-specific base parameter and a channel-specific decay parameter,and each of the interaction terms is based on an interaction strengthparameter, and a channel-specific base parameter and a channel-specificdecay parameter for pair of marketing events, identify a priordistribution for the customer-independent baseline parameter, theinteraction strength parameter, and the channel-specific base parameterand the channel-specific decay parameter, and apply a Bayesianstatistical model to compute updated distributions for thecustomer-independent baseline parameter, the interaction strengthparameter, and the channel-specific base parameter and thechannel-specific decay parameter for each of the corresponding marketingevents based on the plurality of marketing events and the correspondingoutcomes.

A method, apparatus, and non-transitory computer readable medium forcausal marketing attribution are described. Embodiments of the method,apparatus, and non-transitory computer readable medium may receive aplurality of marketing events and corresponding outcomes associated witha plurality of customers, determine a marketing attribution modelcomprising a sum of a plurality channel-specific terms and a sum of aplurality of interaction terms by applying a Bayesian statistical modelusing the plurality of marketing events and the corresponding outcomes,identify a plurality of candidate marketing strategies, and select oneof the plurality of candidate marketing strategies using the marketingattribution model.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of a process to optimize a marketing strategybased on an attribution model according to aspects of the presentdisclosure.

FIG. 2 shows an example of a process for determining a probability of atarget outcome for the customer according to aspects of the presentdisclosure.

FIG. 3 shows an example of a process to compute the sum of thechannel-specific terms according to aspects of the present disclosure.

FIG. 4 shows an example of a process to compute the sum of theinteraction terms according to aspects of the present disclosure.

FIG. 5 shows an example of a process to determine a probability of atarget outcome for the customer according to aspects of the presentdisclosure.

FIG. 6 shows an example of a process for marketing attribution accordingto aspects of the present disclosure.

FIG. 7 shows an example of a process for generating a marketingattribution interpretation according to aspects of the presentdisclosure.

FIG. 8 shows an example of a process for optimizing a marketing strategyaccording to aspects of the present disclosure.

FIG. 9 shows an example of a marketing attribution system according toaspects of the present disclosure.

FIG. 10 shows an example of a marketing attribution apparatus accordingto aspects of the present disclosure.

DETAILED DESCRIPTION

The present disclosure describes systems and methods for causalmarketing attribution that captures the synergistic effects betweendifferent marketing channels. In a multi-channel marketing environment,a purchase decision is often based on a series of interactions such ase-mail, mobile, display advertising, and social media. Theseinteractions have both direct, and indirect, influence on the finaldecisions of the customer. A marketer is responsible for understandinghow the various marketing efforts affect a customer's final purchasingdecision to maximize sales. For example, a marketer can optimize anadvertising budget by using a combination of interacting marketingchannels.

In many cases, the influence of each channel may be difficult to detect.For example, it may be difficult to distinguish between the effects of atelevision ad, a marketing email, and an online ad if a customer hasbeen exposed to all of these marketing channels at different times. Ifpurchase decisions are attributed to the wrong marketing channels,marketing efforts may be directed to channels that are inefficient. Thismay result in a loss of time and money.

A variety of methods may be used to attribute influence to differentmarketing channels. For example, in First Touch Attribution, the entirecredit for a desired result (i.e., a sale) may be attributed to thefirst marketing event a customer is exposed to. In Last TouchAttribution, the entire credit is applied to the last event. In EqualTouch Attribution, equal credit may be applied to all marketing events.However, none of these approaches account for the timing of the eventsin relation to the sale (or other target outcome) or for interactioneffects between events.

Therefore, marketers may utilize more sophisticated marketingattribution models. For example, in some cases model parameters may beexponential in the number of touches and number of events per customer(i.e., to represent diminishing returns for multiple touches). In somecases, the average of the lost value of not using a particular adchannel is calculated over multiple possible sets of ads. In othercases, models assign transition probabilities between touches and sales.This allows for an attribution to be computed as the ratio ofconditional probabilities of a sale with and without an ad,respectively.

These more sophisticated methods may target carry-over and shapeeffects, and may focus on time-series modeling of the delayed customerresponse for each separate ad channel, using a non-parametric functions.However, in some cases even these models fail to capture the specificmechanisms by which a customer arrives at a purchase decision. Forexample, in some models the interactions between two ad channels may notbe considered. Furthermore, due to the lack of intuition of traditionalmarket attribution models, determining the influence of each channel maybe difficult.

Therefore, embodiments of the present disclosure enable causal marketingattribution that takes into account the interaction among differentmarketing channels, as well as the decay of marketing influence overtime. Marketing influence is attributed to different marketing channelsusing a model that takes into account the timing and interaction ofdifferent marketing events. First, an estimate of the behavior of acustomer when exposed to a variety of marketing channels is determined.Second, using probabilistic models of customer behavior, the behavioralestimates from the first stage influence each channel of the outputestimated attributions on the various marketing channels.

Embodiments of the present disclosure may be used in a marketingpipeline (e.g., between the data-collection and ad-assignmentoptimization stage). This allows the marketer to better calibrate adcampaigns.

For example, at least one embodiment of the present disclosure includessystems and methods for attributing marketing influence to differentmarketing channels using a model that takes into account the timing andinteraction of different marketing events. The described systems andmethods are based at least in part on a probabilistic model forpredicting the likelihood of a target outcome based on a time series ofmarketing events. A causal attribution system may identify a pluralityof marketing events associated with a customer, compute the sum of aplurality of channel-specific terms corresponding to the plurality ofmarketing events, compute the sum of a plurality of interaction terms,and determine the probability of a target outcome for the customer basedon the computed sums. Each of the channel-specific terms and theinteraction terms also includes a decay parameter. The model may alsoinclude a customer-independent baseline parameter and acustomer-heterogeneity parameter.

Furthermore, the model provides parameters that have a naturalinterpretation, making it easier for marketers to utilize the parametersof the model in a way that helps optimize a marketing campaign.Additionally, the model may provide distributions over marketingattributions reflect uncertainty inherent in the data given data fromthe recent marketing history of a product.

Embodiments of the present disclosure model the synergistic effectsbetween marketing channels in a manner such that counterfactualquestions can be answered assuming flexible, probabilistic customerbehavior with model marketing characteristics. The model marketingcharacteristics may include the direct effect of a marketing channelinteraction and the decay of the direct effect of a marketing channelinteraction, which informs of the half-life of an ad. Additionally,other model marketing characteristics may include the interaction orsynergistic effects between ads, modeling customer heterogeneity(impulsive vs. careful buyers), control observed features of a customer,and generate variance (or error) estimates for all estimated parameters.

The following terms are used throughout the present disclosure:

A “marketing event” refers to the exposure of a customer to an event inone or more marketing channels, such as when a customer views an onlinead, reads an advertising email, or sees a product placement in a video.

The term “marketing attribution” refers to the process of identifyingthe influence of different marketing events (e.g., an emailadvertisement or a video advertisement) in a multi-channel marketingenvironment.

A “marketing attribution model” refers to an equation, algorithm, orsystem that captures and implements marketing attribution information.For example, a marketing attribution model may be an equation whoseparameters represent the influence of different marketing channels, andwhich enables predictions to be made regarding the influence of a seriesof marketing events on a customer's purchase decision.

A “channel-specific term” is a term in a marketing attribution modelthat includes a “channel-specific base parameter” representing theunderlying influence of a marketing channel and a “decay parameter”representing how quickly the influence of a marketing event decays overtime. For example, a particular channel-specific term may represent howmuch more likely a customer is to buy a product if they read anadvertising email two days past.

An “interaction term” is a term in a marketing attribution model thatincludes an “interaction strength parameter” representing how theexistence of multiple marketing events impacts each other as well as thechannel-specific base parameters and decay parameters for a pair ofevents. For example, particular interaction terms might represent thedifference between the impact of both reading an email and viewing anonline ad as compared to the sum of the influence of those events takenseparately. In many cases, the interaction strength parameter isnegative, meaning that exposure to multiple marketing channels hasdiminishing returns.

A “customer-independent baseline parameter” is a term in a marketingattribution model that may represent the baseline probability that acustomer will make a purchase (or some other target outcome) withoutexposure to any marketing.

A “customer-heterogeneity parameter” is a term in a marketingattribution model that may represent the difference between differentcustomers with regard to the likelihood that they will purchase aproduct or service.

A “Bayesian statistical model” is a model where probability expresses adegree of belief in an event. Bayesian statistics are largely based onthe following result in conditional probability:P(A|B)=P(B|A)P(A)/P(B)  (1)

That is, the probability of a first event given a second event is equalto the probability of the second event given the first, multiplied bythe probability of the first event divided by the probability of thesecond event.

A Markov Chain Monte Carlo (MCMC) is a statistical method comprising aclass of algorithms used for sampling from a probability distribution.

FIG. 1 shows an example of a process to optimize a marketing strategybased on an attribution model according to aspects of the presentdisclosure. The process depicted in FIG. 1 includes operations performedby a user (i.e., a marketing decision maker) and a marketing attributionserver as described in FIGS. 9 and 10.

Marketing attribution enables the interpretation of the influence of thevarious marketing channels on the customer's decision process. Marketingattribution can utilize traditional market attribution models thatassign influence to each marketing channel in an overly simplistic,rule-based manner as described above (i.e., First Touch Attribution,Last Touch Attribution, or Equal Touch Attribution). However, none ofthese approaches account for the timing of the events in relation to thesale (or other target outcome) or for interaction effects betweenevents. Thus, FIG. 1 provides a marketing attribution process that takesinto account time decay and interactions among marketing channels.

At operation 100 the marketing attribution system collects marketing andtransactional information. In some cases, this operation may refer to,or be performed by, a user as described with reference to FIG. 9.

At operation 105, the marketing attribution system estimates customerbehavior. At operation 110, the marketing attribution system attributesvalue to each marketing channel. In some cases, the operations of blocks105 and 110 may refer to, or be performed by, a server as described withreference to FIGS. 9 and 10.

At operation 115, the marketing attribution system optimizes marketingstrategy based on an attribution model. In some cases, this operationmay refer to, or be performed by, a user as described with reference toFIG. 9.

The process described in FIG. 1 provides generalized framework forconsidering attribution techniques with a likelihood-based model. Insome cases, position-based methods are used that take into account therelative positions of different marketing events. Moreover, simpledesign choices allow for the incorporation of time-dependence andinteraction between the models. Furthermore, the marketing attributionmodel uses parameters that correspond to many real-world attributions ofthe marketing process. For example, a Bayesian formulation provides amodel validation measure of the likelihood score. Metrics such ascustomer fatigue and heterogeneity, ad touch decay with respect to time,and interactive effects of multiple channels are provided in thecalculation.

FIG. 2 shows an example of a process for determining a probability of atarget outcome for the customer according to aspects of the presentdisclosure. In some examples, these operations may be performed by asystem including a processor executing a set of codes to controlfunctional elements of an apparatus. Additionally or alternatively, theprocesses may be performed using special-purpose hardware. Generally,these operations may be performed according to the methods and processesdescribed in accordance with aspects of the present disclosure. Forexample, the operations may be composed of various substeps or may beperformed in conjunction with other operations described herein.

At operation 200, the marketing attribution system receives a set ofmarketing events associated with one or more customers. For example, themarketing events may be gathered using a marketing analytics platformsuch as Adobe Experience Cloud©. In some cases, this operation may referto, or be performed by, an input component as described with referenceto FIG. 10.

At operation 205, the marketing attribution system computes a sum of aset of channel-specific terms corresponding to the set of marketingevents, where each of the set of channel-specific terms includes achannel-specific base parameter and a channel-specific decay parameter.In some cases, this operation may refer to, or be performed by, achannel-specific component as described with reference to FIG. 10. Anexample of a process to compute the sum of the channel-specific terms isdescribed in more detail in FIG. 3.

At operation 210, the marketing attribution system computes a sum of aset of interaction terms, where each interaction term includes a productof a pair of channel-specific terms. In some cases, this operation mayrefer to, or be performed by, an interaction component as described withreference to FIG. 10. An example of a process to compute the sum of theinteraction terms is described in more detail in FIG. 4.

At operation 215, the marketing attribution system determines aprobability of a target outcome for the customer based on the sum of theset of channel-specific terms and the sum of the set of interactionterms. In some cases, this operation may refer to, or be performed by, aprobability component as described with reference to FIG. 10. An exampleof a process to determine a probability of a target outcome for thecustomer described in more detail in FIG. 5.

As an example, the following marketing attribution model may be used:P(r=1|a ₁ ,t ₁ , . . . ,a _(k) ,t _(k))=g(μ+b _(i)+Σ_(i)β_(a) _(i) λ_(a)_(i) ^(t) ^(i) +Σ_(i≠j)γβ_(a) _(i) β_(a) _(j) λ_(a) _(i) ^(t) ^(i) λ_(a)_(j) ^(t) ^(j) )  (2)

The parameters may correspond to distinct real-world values. Theparameter μ captures the customer-independent baseline chance ofpurchase. b_(i) reflects customer-heterogeneity of the ith customer,which is forced to be 0-mean. β_(a),λ_(a) are the channel-specific basemagnitude and decay parameters, respectively. γ controls the magnitudeand direction of interactive effects. g is a link function thatspecifies the class of predictions; e.g. either continuous, binary, orotherwise. In some cases, the marketing attribution model can furtherincorporate elements such as conditioning on previous sales and otherobserved characteristics of a customer.

According to at least one embodiment, the parameters of the model may becomputed using a Bayesian statistical model. In other embodiments, theparameters may be computed using a gradient descent algorithm. Furtherdetail regarding computing the parameters is described below withreference to FIGS. 6 and 7.

Assigning counterfactual meaning to the present disclosure's predictionsmay be accomplished using additional modifications and assumptions. Anadditional term Σ_(k=1) ^(K−1)θ_(k)y_(k,i) may be used for theprediction of the outcome y_(k). This incorporates a linear combinationof the previous sales into the model. The correct specification of thepresent disclosure is assumed. The assignment of touches at any timestep and the time-differences between touches are independent of thefuture sales given the past sales, and ad touch history is assumed.Under these assumptions, the attributions become counterfactual innature.

In some cases, a marketing event may be presented to a customer based onthe probability of the target outcome. For example, one or moreadvertisements in different advertising channels may be presented to thecustomer.

FIG. 3 shows an example of a process to compute the sum of thechannel-specific terms according to aspects of the present disclosure.In some examples, these operations may be performed by a systemincluding a processor executing a set of codes to control functionalelements of an apparatus. Additionally or alternatively, the processesmay be performed using special-purpose hardware. Generally, theseoperations may be performed according to the methods and processesdescribed in accordance with aspects of the present disclosure. Forexample, the operations may be composed of various substeps or may beperformed in conjunction with other operations described herein.

At operation 300, the marketing attribution system determineschannel-specific base parameters (e.g., β_(a)). The channel-specificbase parameters may intuitively correspond to the initial influence of aparticular marketing channel on a purchase decision. In some cases, thisoperation may refer to, or be performed by, a channel-specific componentas described with reference to FIG. 10.

At operation 305, the marketing attribution system determineschannel-specific decay parameters (e.g., λ_(a)). The channel-specificdecay parameters may intuitively represent the rate at which theinfluence of a channel decays over time. In some cases, this operationmay refer to, or be performed by, a channel-specific component asdescribed with reference to FIG. 10.

At operation 310, the marketing attribution system identifies a timevalue for each channel specific event (i.e., t_(i)). The time value mayrepresent the time at which a marketing event occurred. In some cases,the time is expressed in terms of how far in time the marketing event isfrom a purchase decision. In some cases, this operation may refer to, orbe performed by, a channel-specific component as described withreference to FIG. 10.

At operation 315, the marketing attribution system multiplies eachchannel-specific base parameter by the corresponding channel-specificdecay parameter raised to the power of the time value to producechannel-specific terms (e.g., β_(a) _(i) λ_(a) _(i) ^(t) ^(i) ). Thus,the channel-specific terms may intuitively represent the actualinfluence of a marketing event at the time of a purchase decision. Insome cases, this operation may refer to, or be performed by, achannel-specific component as described with reference to FIG. 10.

At operation 320, the marketing attribution system computes the sum ofthe channel-specific terms (e.g., Σ_(i)β_(a) _(i) λ_(a) _(i) ^(t) ^(i)). The sum of the channel-specific terms may represent the totalinfluence of individual marketing channels without taking into accountthe interaction between different channels. In some cases, thisoperation may refer to, or be performed by, a channel-specific componentas described with reference to FIG. 10.

FIG. 4 shows an example of a process to compute the sum of theinteraction terms according to aspects of the present disclosure. Insome examples, these operations may be performed by a system including aprocessor executing a set of codes to control functional elements of anapparatus. Additionally or alternatively, the processes may be performedusing special-purpose hardware. Generally, these operations may beperformed according to the methods and processes described in accordancewith aspects of the present disclosure. For example, the operations maybe composed of various substeps or may be performed in conjunction withother operations described herein.

Operations 400 through 415 may correspond to steps 300 through 315described with reference to FIG. 3. However, while FIG. 3 describes howthese terms are used to compute channel-specific terms, FIG. 4 describeshow they are used to compute interaction terms.

At operation 400, the marketing attribution system determineschannel-specific base parameters (e.g., β_(a)). In some cases, thisoperation may refer to, or be performed by, a channel-specific componentor an interaction component as described with reference to FIG. 10.

At operation 405, the marketing attribution system determineschannel-specific decay parameters (e.g., λ_(a)). In some cases, thisoperation may refer to, or be performed by, a channel-specific componentor an interaction component as described with reference to FIG. 10.

At operation 410, the marketing attribution system identifies a timevalue for each channel specific event (i.e., t_(i)). In some cases, thisoperation may refer to, or be performed by, a channel-specific componentor an interaction component as described with reference to FIG. 10.

At operation 415, the marketing attribution system multiplies eachchannel-specific base parameter by the corresponding channel-specificdecay parameter raised to the power of the time to producechannel-specific terms (e.g., β_(a) _(i) λ_(a) _(i) ^(t) ^(i) ). In somecases, this operation may refer to, or be performed by, achannel-specific component or an interaction component as described withreference to FIG. 10.

At operation 420, the marketing attribution system multiplies each pairof channel specific terms by an interaction strength parameter toproduce interaction terms (e.g., γβ_(a) _(i) β_(a) _(j) λ_(a) _(i) ^(t)^(i) λ_(a) _(j) ^(t) ^(j) ). The interaction terms may represent theimpact that combinations of marketing events have on each other. Forexample, in many cases the interaction terms are negative because theimpact of being exposed to a second marketing event may be less than ifthe marketing event were presented in isolation. In some cases, thisoperation may refer to, or be performed by, an interaction component asdescribed with reference to FIG. 10.

At operation 425, the marketing attribution system computes the sum ofthe interaction terms (e.g., Σ_(i≠j)γβ_(a) _(i) β_(a) _(j) λ_(a) _(i)^(t) ^(i) λ_(a) _(j) ^(t) ^(j) ). The sum of the interaction terms mayintuitively represent the overall impact of the combinations ofmarketing events on each other. In some cases, the sum of theinteraction terms is negative, meaning that the effect of many differentmarketing events in combination may not be equal to the sum of theindividual events in isolation (i.e., there may be diminishing returnsto additional marketing). In some cases, this operation may refer to, orbe performed by, an interaction component as described with reference toFIG. 10.

FIG. 5 shows an example of a process to determine a probability of atarget outcome for the customer according to aspects of the presentdisclosure. In some examples, these operations may be performed by asystem including a processor executing a set of codes to controlfunctional elements of an apparatus. Additionally or alternatively, theprocesses may be performed using special-purpose hardware. Generally,these operations may be performed according to the methods and processesdescribed in accordance with aspects of the present disclosure. Forexample, the operations may be composed of various substeps or may beperformed in conjunction with other operations described herein.

At operation 500, the marketing attribution system determines acustomer-independent baseline parameter (e.g., μ). In some cases, thisoperation may refer to, or be performed by, a probability component asdescribed with reference to FIG. 10.

At operation 505, the marketing attribution system determines acustomer-heterogeneity parameter (e.g., b_(i)). In some cases, thisoperation may refer to, or be performed by, a probability component asdescribed with reference to FIG. 10.

At operation 510, the marketing attribution system computes a sum ofchannel-specific terms (e.g., Σ_(i)β_(a) _(i) λ_(a) _(i) ^(t) ^(i) ). Insome cases, this operation may refer to, or be performed by, achannel-specific component as described with reference to FIG. 10.

At operation 515, the marketing attribution system computes a sum ofinteraction terms (e.g., Σ_(i≠j)γβ_(a) _(i) β_(a) _(j) λ_(a) _(i) ^(t)^(i) λ_(a) _(j) ^(t) ^(j) ). In some cases, this operation may refer to,or be performed by, an interaction component as described with referenceto FIG. 10.

At operation 520, the marketing attribution system adds thecustomer-independent baseline parameter, the customer-heterogeneityparameter, the sum of channel-specific terms and the sum of interactionterms to produce a customer-specific sum (e.g., μ+b_(i)+Σ_(i)β_(a) _(i)λ_(a) _(i) ^(t) ^(i) +Σ_(i≠j)γβ_(a) _(i) β_(a) _(j) λ_(a) _(i) ^(t) ^(i)λ_(a) _(j) ^(t) ^(j) ). In some cases, this operation may refer to, orbe performed by, a probability component as described with reference toFIG. 10.

At operation 525, the marketing attribution system applies a linkfunction to the customer-specific sum to produce the probability of thetarget outcome (e.g., g(μ+b_(i)+Σ_(i)β_(a) _(i) λ_(a) _(i) ^(t) ^(i)+Σ_(i≠j)γβ_(a) _(i) β_(a) _(j) λ_(a) _(i) ^(t) ^(i) λ_(a) _(j) ^(t) ^(j))). In some cases, this operation may refer to, or be performed by, aprobability component as described with reference to FIG. 10.

FIG. 6 shows an example of a process for marketing attribution accordingto aspects of the present disclosure. In some examples, these operationsmay be performed by a system including a processor executing a set ofcodes to control functional elements of an apparatus. Additionally oralternatively, the processes may be performed using special-purposehardware. Generally, these operations may be performed according to themethods and processes described in accordance with aspects of thepresent disclosure. For example, the operations may be composed ofvarious substeps, or may be performed in conjunction with otheroperations described herein.

At operation 600, the marketing attribution system receives a set ofmarketing events and corresponding outcomes associated with a set ofcustomers. In some cases, this operation may refer to, or be performedby, an input component as described with reference to FIG. 10.

At operation 605, the marketing attribution system identifies amarketing attribution model including a customer-independent baselineparameter, a set of customer-heterogeneity parameters, a sum of a set ofchannel-specific terms, and a sum of a set of interaction terms, whereeach of the channel-specific terms is based on a channel-specific baseparameter and a channel-specific decay parameter, and each of theinteraction terms is based on an interaction strength parameter, and achannel-specific base parameter and a channel-specific decay parameterfor pair of marketing events. In some cases, this operation may referto, or be performed by, a probability component as described withreference to FIG. 10.

At operation 610, the marketing attribution system identifies a priordistribution for the customer-independent baseline parameter, theinteraction strength parameter, and the channel-specific base parameterand the channel-specific decay parameter. In some cases, this operationmay refer to, or be performed by, a statistical component as describedwith reference to FIG. 10.

At operation 615, the marketing attribution system applies a Bayesianstatistical model to compute updated distributions for thecustomer-independent baseline parameter, the interaction strengthparameter, and the channel-specific base parameter and thechannel-specific decay parameter for each of the corresponding marketingevents based on the set of marketing events and the correspondingoutcomes. In some cases, this operation may refer to, or be performedby, a statistical component as described with reference to FIG. 10.

In some examples, the Bayesian statistical model may be computed using astatistical platform such as R, Stan, Winbugs, pytorch, or any othersuitable statistical modelling platform. In some embodiments, a gradientdescent or gradient ascent method may be used as an alternative to theBayesian statistical model. A gradient descent is a first-orderiterative optimization algorithm for finding the minimum of a function.A gradient decent algorithm finds a local minimum by starting at a givenpoint and then moving to a new point based on the direction (andmagnitude) of the gradient at that point. A gradient ascent is a similaralgorithm used to find a local maximum.

In some examples, the prior information in the Bayesian statisticalmodel may have a wide and flat distribution, providing minimal priorinformation into the model. For example, customer-heterogeneity may bemodeled as a random-effect b_(i) with variance regularized to be small:b_(i)˜N (0, σ_(b) ²), where σ_(b)˜exp(0.5) and has a wide and flatdistribution, providing minimal prior information. A wide non-negativedistribution of prior information on the base magnitudes β_(a)˜exp(10),where the sign-restriction on β_(s) reflects the knowledge that all adshave a non-negative effect when ads occur without any interactions,provides minimal information as well. Additionally, wide priorinformation on the interaction term and the baseline γ˜N (0,10), μ˜N(0,10) and a flat positive prior information on the decay parametersγ_(a)˜Unif (0,1) provide minimal information.

In an illustrative example, a regression setup is used, where the linkfunction g is the identity function, and no random effects or baselineparameters are used. Data may be sampled for a large number of customers(e.g., 10,000 or more), where each customer has 10 actions equallysampled from a random set of 5 actions (actions can be sample more thanonce). The sample time-differences between the touches from theexponential δt˜exp(1). The parameters β,γ˜N (0,1) and λ˜β(1,1). Theoutcome y is then generated according to the present disclosure.

FIG. 7 shows an example of a process for generating a marketingattribution interpretation according to aspects of the presentdisclosure. In some examples, these operations may be performed by asystem including a processor executing a set of codes to controlfunctional elements of an apparatus. Additionally or alternatively, theprocesses may be performed using special-purpose hardware. Generally,these operations may be performed according to the methods and processesdescribed in accordance with aspects of the present disclosure. Forexample, the operations may be composed of various substeps or may beperformed in conjunction with other operations described herein.

At operation 700, the marketing attribution system receives a set ofmarketing events and corresponding outcomes associated with a set ofcustomers. In some cases, this operation may refer to, or be performedby, an input component as described with reference to FIG. 10.

At operation 705, the marketing attribution system identifies amarketing attribution model including a customer-independent baselineparameter, a set of customer-heterogeneity parameters, a sum of a set ofchannel-specific terms, and a sum of a set of interaction terms, whereeach of the channel-specific terms is based on a channel-specific baseparameter and a channel-specific decay parameter, and each of theinteraction terms is based on an interaction strength parameter, and achannel-specific base parameter and a channel-specific decay parameterfor pair of marketing events. In some cases, this operation may referto, or be performed by, a probability component as described withreference to FIG. 10.

At operation 710, the marketing attribution system identifies a priordistribution for the customer-independent baseline parameter, theinteraction strength parameter, and the channel-specific base parameterand the channel-specific decay parameter. In some cases, this operationmay refer to, or be performed by, a statistical component as describedwith reference to FIG. 10.

At operation 715, the marketing attribution system applies a Bayesianstatistical model to compute updated distributions for thecustomer-independent baseline parameter, the interaction strengthparameter, and the channel-specific base parameter and thechannel-specific decay parameter for each of the corresponding marketingevents based on the set of marketing events and the correspondingoutcomes. In some cases, this operation may refer to, or be performedby, a statistical component as described with reference to FIG. 10.

At operation 720, the marketing attribution system generates a marketingattribution interpretation of the baseline impact of a marketing action,the time decay rate of the marketing action, the strength of aninteraction between marketing actions, the baseline probability of atarget outcome, or any combination thereof based on the updateddistributions. In some cases, this operation may refer to, or beperformed by, an interpretation component as described with reference toFIG. 10.

Thus, the present disclosure provides interpretable parameters that areinsightful, even when the true model is incorrectly specified.Furthermore, the sale-likelihood formulation may further incorporateelements such as conditioning on previous sales and other observedcharacteristics of a customer.

FIG. 8 shows an example of a process for optimizing a marketing strategyaccording to aspects of the present disclosure. In some examples, theseoperations may be performed by a system including a processor executinga set of codes to control functional elements of an apparatus.Additionally or alternatively, the processes may be performed usingspecial-purpose hardware. Generally, these operations may be performedaccording to the methods and processes described in accordance withaspects of the present disclosure. For example, the operations may becomposed of various substeps or may be performed in conjunction withother operations described herein.

At operation 800, the marketing attribution system receives a set ofmarketing events and corresponding outcomes associated with a set ofcustomers. In some cases, this operation may refer to, or be performedby, an input component as described with reference to FIG. 10.

At operation 805, the marketing attribution system determines amarketing attribution model including a sum of a set of channel-specificterms and a sum of a set of interaction terms by applying a Bayesianstatistical model using the set of marketing events and thecorresponding outcomes. In some cases, this operation may refer to, orbe performed by, a probability component as described with reference toFIG. 10.

At operation 810, the marketing attribution system identifies a set ofcandidate marketing strategies. In some cases, this operation may referto, or be performed by, an optimization component as described withreference to FIG. 10.

At operation 815, the marketing attribution system selects one of thesets of candidate marketing strategies using the marketing attributionmodel. In some cases, this operation may refer to, or be performed by,an optimization component as described with reference to FIG. 10.

FIG. 1 shows an example of a marketing attribution system according toaspects of the present disclosure. The example shown includes server100, user 105, events database 110, and network 115. User 905 mayrepresent a marketing decision maker. User 905 may request marketingattribution information from server 900 based on information stored inthe events database 910. Each of these elements communicates with eachother via network 915. In some cases, server 900 receives eventinformation from the user 905. Server 100 may be an example of, orinclude aspects of, the corresponding element or elements described withreference to FIG. 2. In some examples, the events database 110, theserver 100, or both may be components of a marketing analytics platformsuch as Adobe Experience Cloud©.

FIG. 2 shows an example of a marketing attribution apparatus accordingto aspects of the present disclosure. Server 200 may be an example of,or include aspects of, the corresponding element or elements describedwith reference to FIG. 1. Server 200 may include processor unit 205,memory unit 210, input component 215, channel-specific component 220,interaction component 225, probability component 230, statisticalcomponent 235, interpretation component 240, and optimization component245.

A processor unit 205 may include an intelligent hardware device, (e.g.,a general-purpose processing component, a digital signal processor(DSP), a central processing unit (CPU), a graphics processing unit(GPU), a microcontroller, an application-specific integrated circuit(ASIC), a field-programmable gate array (FPGA), a programmable logicdevice, a discrete gate or transistor logic component, a discretehardware component, or any combination thereof). In some cases, theprocessor may be configured to operate a memory array using a memorycontroller. In other cases, a memory controller may be integrated intoprocessor. The processor may be configured to execute computer-readableinstructions stored in a memory to perform various functions. In someexamples, a processor may include special-purpose components for modemprocessing, baseband processing, digital signal processing, ortransmission processing. In some examples, the processor may comprise asystem-on-a-chip.

A memory unit 210 may store information for various programs andapplications on a computing device. For example, the storage may includedata for running an operating system. The memory may include bothvolatile memory and non-volatile memory. Volatile memory may randomaccess memory (RAM), and non-volatile memory may include read-onlymemory (ROM), flash memory, electrically erasable programmable read-onlymemory (EEPROM), digital tape, a hard disk drive (HDD), and asolid-state drive (SSD). Memory may include any combination of readableand/or writable volatile memories and/or non-volatile memories, alongwith other possible storage devices.

Input component 215 may receive a set of marketing events and/orcorresponding outcomes associated with a customer. For example, inputcomponent 215 may receive marketing information from a marketinganalytics platform including an events database as described withreference to FIG. 9.

Channel-specific component 220 may compute a sum of a set ofchannel-specific terms corresponding to the set of marketing events,where each of the set of channel-specific terms includes achannel-specific base parameter and a channel-specific decay parameter.Channel-specific component 220 may also identify a time valuecorresponding to each of the marketing events. Each of the set ofchannel-specific terms includes a product of the channel-specific decayparameter raised to a power of the time value and the channel-specificbase parameter.

Interaction component 225 may compute a sum of a set of interactionterms, where each interaction term includes a product of a pair ofchannel-specific terms. In some examples, each of the set of interactionterms has a factor including the channel-specific decay parameter raisedto a power of the time value for each channel-specific event of a pairof corresponding marketing events. Interaction component 225 may alsoidentify the interaction strength parameter, where each of a set ofinteraction terms includes the interaction strength parameter as afactor.

Probability component 230 may determine a probability of a targetoutcome for the customer based on the sum of the set of channel-specificterms and the sum of the set of interaction terms. Probability component230 may also identify a customer-independent baseline parameter, acustomer-heterogeneity parameter, and a link function. In some examples,an average of customer-heterogeneity parameters across customers is setat 0.

Thus, probability component 230 may identify a marketing attributionmodel including a customer-independent baseline parameter, a set ofcustomer-heterogeneity parameters, a sum of a set of channel-specificterms, and a sum of a set of interaction terms. Each of thechannel-specific terms are based on a channel-specific base parameterand a channel-specific decay parameter.

In one embodiment, probability component 230 may determine the marketingattribution model based on a Bayesian statistical model using the set ofmarketing events and the corresponding outcomes. In some examples, theBayesian statistical model includes a Markov Chain Monte Carlo (MCMC)method.

Statistical component 235 may identify a prior distribution for thecustomer-independent baseline parameter, the interaction strengthparameter, and the channel-specific base parameter and thechannel-specific decay parameter. Statistical component 235 may alsoapply a Bayesian statistical model to compute updated distributions forthe customer-independent baseline parameter, the interaction strengthparameter, and the channel-specific base parameter and thechannel-specific decay parameter for each of the corresponding marketingevents based on the set of marketing events and the correspondingoutcomes.

In some examples, the prior distributions for the customer-independentbaseline parameter, the interaction strength parameter, and thechannel-specific base parameter for each of the corresponding marketingevents include normal distributions. The prior distributions for thechannel-specific decay parameter for each of the corresponding marketingevents includes a uniform distribution.

Interpretation component 240 may generate a marketing attributioninterpretation of the baseline impact of a marketing action, the timedecay rate of the marketing action, the strength of an interactionbetween marketing actions, the baseline probability of a target outcome,or any combination thereof based on the updated distributions.

Optimization component 245 may identify a set of candidate marketingstrategies. Optimization component 245 may also select one of the set ofcandidate marketing strategies using the marketing attribution model.Optimization component 245 may also apply a gradient descent algorithmto the marketing attribution model, where one of the set of candidatemarketing strategies is selected based on the gradient descentalgorithm.

Accordingly, the present disclosure includes the following embodiments.

A method, apparatus, and non-transitory computer readable medium storingcode for causal marketing attribution are described. Embodiments mayreceive a plurality of marketing events associated with a customer,computing a sum of a plurality of channel-specific terms correspondingto the plurality of marketing events, wherein each of the plurality ofchannel-specific terms comprises a channel-specific base parameter and achannel-specific decay parameter, computing a sum of a plurality ofinteraction terms, wherein each interaction term comprises a product ofa pair of channel-specific terms, and determining a probability of atarget outcome for the customer based on the sum of the plurality ofchannel-specific terms and the sum of the plurality of interactionterms.

Some examples of the method, apparatus, and non-transitory computerreadable medium described above may further include identifying acustomer-independent baseline parameter, wherein the probability of thetarget outcome is based at least in part on the customer-independentbaseline parameter.

Some examples of the method, apparatus, and non-transitory computerreadable medium described above may further include identifying acustomer-heterogeneity parameter, wherein the probability of the targetoutcome is based at least in part on the customer-heterogeneityparameter. In some examples, an average of customer-heterogeneityparameters across customers is set at 0.

Some examples of the method, apparatus, and non-transitory computerreadable medium described above may further include identifying a timevalue corresponding to each of the marketing events, wherein each of theplurality of channel-specific terms comprises a product of thechannel-specific decay parameter raised to a power of the time value andthe channel-specific base parameter. In some examples, each of theplurality of interaction terms has a factor comprising thechannel-specific decay parameter raised to a power of the time value foreach channel-specific event of a pair of corresponding marketing events.

Some examples of the method, apparatus, and non-transitory computerreadable medium described above may further include identifying aninteraction strength parameter, wherein each of the plurality ofinteraction terms comprises the interaction strength parameter as afactor.

A method, apparatus, and non-transitory computer readable medium storingcode for causal marketing attribution are described. Embodiments of themethod may receiving a plurality of marketing events and correspondingoutcomes associated with a plurality of customers, identifying amarketing attribution model comprising a customer-independent baselineparameter, a plurality of customer-heterogeneity parameters, a sum of aplurality of channel-specific terms, and a sum of a plurality ofinteraction terms, wherein each of the channel-specific terms is basedon a channel-specific base parameter and a channel-specific decayparameter, and each of the interaction terms is based on an interactionstrength parameter, and a channel-specific base parameter and achannel-specific decay parameter for pair of marketing events,identifying a prior distribution for the customer-independent baselineparameter, the interaction strength parameter, and the channel-specificbase parameter and the channel-specific decay parameter, and apply aBayesian statistical model to compute updated distributions for thecustomer-independent baseline parameter, the interaction strengthparameter, and the channel-specific base parameter and thechannel-specific decay parameter for each of the corresponding marketingevents based on the plurality of marketing events and the correspondingoutcomes.

In some examples, the prior distributions for the customer-independentbaseline parameter, the interaction strength parameter, and thechannel-specific base parameter for each of the corresponding marketingevents comprise normal distributions and the prior distributions for thechannel-specific decay parameter for each of the corresponding marketingevents comprises a uniform distribution. In some examples, the Bayesianstatistical model comprises a Markov Chain Monte Carlo (MCMC) method.

Some examples of the method, apparatus, and non-transitory computerreadable medium described above may further include generating amarketing attribution interpretation of the baseline impact of amarketing action, the time decay rate of the marketing action, thestrength of interaction between marketing actions, the baselineprobability of a target outcome, or any combination thereof based on theupdated distributions.

A method, apparatus, and non-transitory computer readable medium storingcode for causal marketing attribution are described. Embodiments of themethod may receiving a plurality of marketing events and correspondingoutcomes associated with a plurality of customers, determining amarketing attribution model comprising a sum of a pluralitychannel-specific terms and a sum of a plurality of interaction terms byapplying a Bayesian statistical model using the plurality of marketingevents and the corresponding outcomes, identifying a plurality ofcandidate marketing strategies, and selecting one of the plurality ofcandidate marketing strategies using the marketing attribution model.

Some examples of the method, apparatus, and non-transitory computerreadable medium described above may further include apply a gradientdescent algorithm to the marketing attribution model, wherein the one ofthe plurality of candidate marketing strategies is selected based on thegradient descent algorithm.

The description and drawings described herein represent exampleconfigurations and do not represent all the implementations within thescope of the claims. For example, the operations and steps may berearranged, combined or otherwise modified. Also, structures and devicesmay be represented in the form of block diagrams to represent therelationship between components and avoid obscuring the describedconcepts. Similar components or features may have the same name but mayhave different reference numbers corresponding to different figures.

Some modifications to the disclosure may be readily apparent to thoseskilled in the art, and the principles defined herein may be applied toother variations without departing from the scope of the disclosure.Thus, the disclosure is not limited to the examples and designsdescribed herein, but is to be accorded the broadest scope consistentwith the principles and novel features disclosed herein.

The described methods may be implemented or performed by devices thatinclude a general-purpose processor, a digital signal processor (DSP),an application specific integrated circuit (ASIC), a field programmablegate array (FPGA) or other programmable logic device, discrete gate ortransistor logic, discrete hardware components, or any combinationthereof. A general-purpose processor may be a microprocessor, aconventional processor, controller, microcontroller, or state machine. Aprocessor may also be implemented as a combination of computing devices(e.g., a combination of a DSP and a microprocessor, multiplemicroprocessors, one or more microprocessors in conjunction with a DSPcore, or any other such configuration). Thus, the functions describedherein may be implemented in hardware or software and may be executed bya processor, firmware, or any combination thereof. If implemented insoftware executed by a processor, the functions may be stored in theform of instructions or code on a computer-readable medium.

Computer-readable media includes both non-transitory computer storagemedia and communication media including any medium that facilitatestransfer of code or data. A non-transitory storage medium may be anyavailable medium that can be accessed by a computer. For example,non-transitory computer-readable media can comprise random access memory(RAM), read-only memory (ROM), electrically erasable programmableread-only memory (EEPROM), compact disk (CD) or other optical diskstorage, magnetic disk storage, or any other non-transitory medium forcarrying or storing data or code.

Also, connecting components may be properly termed computer-readablemedia. For example, if code or data is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technology suchas infrared, radio, or microwave signals, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technology are included inthe definition of medium. Combinations of media are also included withinthe scope of computer-readable media.

In this disclosure and the following claims, the word “or” indicates aninclusive list such that, for example, the list of X, Y, or Z means X orY or Z or XY or XZ or YZ or XYZ. Also the phrase “based on” is not usedto represent a closed set of conditions. For example, a step that isdescribed as “based on condition A” may be based on both condition A andcondition B. In other words, the phrase “based on” shall be construed tomean “based at least in part on.”

What is claimed is:
 1. A method for causal marketing attribution, themethod comprising: receiving a request from a user for marketingattribution information for a first marketing channel and a secondmarketing channel; collecting, by a marketing analytics platform, firstmarketing information corresponding to a first set of marketing events,wherein the first marketing information comprises timing information forcustomer exposures to the first marketing channel comprising onlineadvertising; collecting, by the marketing analytics platform, secondmarketing information corresponding to a second set of marketing events,wherein the second marketing information comprises timing informationfor customer exposures to the second marketing channel, and wherein thesecond marketing channel is different from the first marketing channel;identifying a time series corresponding to the first set of marketingevents and the second set of marketing events; computing a sum of aplurality of channel-specific terms corresponding to each of the firstset of marketing events and each of the second set of marketing events,wherein each channel-specific term of the plurality of channel-specificterms comprises a channel-specific base parameter and a channel-specificdecay parameter raised to a power of a corresponding time value;computing a sum of a plurality of interaction terms, wherein eachinteraction term of the plurality of interaction terms comprises aproduct of a pair of channel-specific terms and at least one interactionterm of the plurality of interaction terms corresponds to the first setof marketing events and the second set of marketing events; determininga probability of a target outcome for a customer based on the sum of theplurality of channel-specific terms, the sum of the plurality ofinteraction terms, and the time series; identifying, by the marketinganalytics platform, a marketing attribution model for the first channelbased at least in part on the probability of the target outcome, whereinthe marketing attribution model identifies an influence of the firstmarketing channel on a behavior of the customer based at least in parton the second marketing channel; and providing, by the marketinganalytics platform to the user, the marketing attribution information,wherein the marketing attribution information is implemented by themarketing attribution model.
 2. The method of claim 1, furthercomprising: identifying a customer-independent baseline parameter,wherein the probability of the target outcome is based at least in parton the customer-independent baseline parameter.
 3. The method of claim1, further comprising: identifying a customer-heterogeneity parameter,wherein the probability of the target outcome is based at least in parton the customer-heterogeneity parameter.
 4. The method of claim 3,wherein: an average of customer-heterogeneity parameters acrosscustomers is set at
 0. 5. The method of claim 1, wherein: eachchannel-specific term of the plurality of channel-specific termscomprises a product of the channel-specific decay parameter raised tothe power of the corresponding time value and the channel-specific baseparameter.
 6. The method of claim 5, wherein: each interaction term ofthe plurality of interaction terms has a factor comprising thechannel-specific decay parameter raised to the power of thecorresponding time value for each channel-specific event of a pair ofcorresponding marketing events.
 7. The method of claim 1, furthercomprising: identifying an interaction strength parameter, wherein eachinteraction term of the plurality of interaction terms comprises theinteraction strength parameter as a factor.
 8. A method for causalmarketing attribution, the method comprising: collecting, by a marketinganalytics platform, first marketing information corresponding to a firstset of marketing events, wherein the first marketing informationcomprises timing information for customer exposures to a first marketingchannel comprising online advertising; collecting, by the marketinganalytics platform, second marketing information corresponding to asecond set of marketing events, wherein the second marketing informationcomprises timing information for customer exposures to a secondmarketing channel, and wherein the second marketing channel is differentfrom the first marketing channel; identifying a time seriescorresponding to the first set of marketing events and the second set ofmarketing events; computing a sum of a plurality of channel-specificterms corresponding to each of the first set of marketing events andeach of the second set of marketing events, wherein eachchannel-specific term of the plurality of channel-specific termscomprises a channel-specific base parameter and a channel-specific decayparameter raised to a power of a corresponding time value; computing asum of a plurality of interaction terms, wherein each interaction termof the plurality of interaction terms comprises a product of a pair ofchannel-specific terms and at least one interaction term of theplurality of interaction terms corresponds to the first set of marketingevents and the second set of marketing events; determining a probabilityof a target outcome for a customer based on the sum of the plurality ofchannel-specific terms, the sum of the plurality of interaction terms,and the time series; identifying, by the marketing analytics platform, amarketing attribution model for the first channel based at least in parton the probability of the target outcome, wherein the marketingattribution model identifies an influence of the first marketing channelon a behavior of the customer based at least in part on the secondmarketing channel; and providing, by the marketing analytics platform toa user, marketing attribution information, wherein the marketingattribution information is implemented by the marketing attributionmodel.
 9. The method of claim 8, further comprising: identifying acustomer-independent baseline parameter, wherein the probability of thetarget outcome is based at least in part on the customer-independentbaseline parameter.
 10. The method of claim 8, further comprising:identifying a customer-heterogeneity parameter, wherein the probabilityof the target outcome is based at least in part on thecustomer-heterogeneity parameter.
 11. The method of claim 10, wherein:an average of customer-heterogeneity parameters across customers is setat
 0. 12. The method of claim 8, wherein: each channel-specific term ofthe plurality of channel-specific terms comprises a product of thechannel-specific decay parameter raised to the power of thecorresponding time value and the channel-specific base parameter. 13.The method of claim 12, wherein: each interaction term of the pluralityof interaction terms has a factor comprising the channel-specific decayparameter raised to the power of the corresponding time value for eachchannel-specific event of a pair of corresponding marketing events. 14.The method of claim 8, further comprising: identifying an interactionstrength parameter, wherein each interaction term of the plurality ofinteraction terms comprises the interaction strength parameter as afactor.
 15. A method for causal marketing attribution, the methodcomprising: receiving a request from a user for marketing attributioninformation for a first marketing channel and a second marketingchannel; collecting, by a marketing analytics platform: first marketinginformation corresponding to a first set of marketing events, whereinthe first marketing information comprises timing information forcustomer exposures to the first marketing channel comprising onlineadvertising; and second marketing information corresponding to a secondset of marketing events, wherein the second marketing informationcomprises timing information for customer exposures to the secondmarketing channel, and wherein the second marketing channel is differentfrom the first marketing channel; computing a sum of a plurality ofchannel-specific terms corresponding to each of the first set ofmarketing events and each of the second set of marketing events, whereineach channel-specific term of the plurality of channel-specific termscomprises a channel-specific base parameter and a channel-specific decayparameter raised to a power of a corresponding time value; computing asum of a plurality of interaction terms, wherein each interaction termof the plurality of interaction terms comprises a product of a pair ofchannel-specific terms and at least one interaction term of theplurality of interaction terms corresponds to the first set of marketingevents and the second set of marketing events; identifying, by themarketing analytics platform, a marketing attribution model for thefirst channel, wherein the marketing attribution model identifies aninfluence of the first marketing channel on a behavior of the customerbased at least in part on the second marketing channel; and providing,by the marketing analytics platform to the user, the marketingattribution information, wherein the marketing attribution informationis implemented by the marketing attribution model.
 16. The method ofclaim 15, further comprising: identifying a customer-independentbaseline parameter.
 17. The method of claim 15, further comprising:identifying a customer-heterogeneity parameter.
 18. The method of claim17, wherein: an average of customer-heterogeneity parameters acrosscustomers is set at
 0. 19. The method of claim 15, wherein: eachchannel-specific term of the plurality of channel-specific termscomprises a product of the channel-specific decay parameter raised tothe power of the corresponding time value and the channel-specific baseparameter.
 20. The method of claim 19, wherein: each interaction term ofthe plurality of interaction terms has a factor comprising thechannel-specific decay parameter raised to the power of thecorresponding time value for each channel-specific event of a pair ofcorresponding marketing events.